


import importlib.util
import os
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import (
    is_accelerate_available,
    is_flax_available,
    is_safetensors_available,
    is_tf_available,
    is_torch_available,
)
from . import BaseTransformersCLICommand







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase				)    ->				Union[str, Any]:
  return EnvironmentCommand()







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase				)    ->				List[str]:
  return EnvironmentCommand(args.accelerate_config_file				)


class 		__magic_name__						(snake_case_				):





     '''simple docstring'''



     @staticmethod
     def 	SCREAMING_SNAKE_CASE__						(					_a:ArgumentParser   ):
       snake_case__						= parser.add_parser('''env'''   )
       download_parser.set_defaults(func=_a   )
       download_parser.add_argument(
           '''--accelerate-config_file'''     ,		default=_a     ,		help='''The accelerate config file to use for the default values in the launching script.'''     ,		)
       download_parser.set_defaults(func=_a   )

     def __init__(					self:List[str]     ,		_a:Dict     ,		*_a:Dict   ):
       snake_case__						= accelerate_config_file

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict   ):
       snake_case__						= '''not installed'''
       if is_safetensors_available():
         import safetensors

         snake_case__						= safetensors.__version__
       elif importlib.util.find_spec('''safetensors'''   ) is not None:
         import safetensors

         snake_case__						= F"""{safetensors.__version__} but is ignored because of PyTorch version too old."""

       snake_case__						= '''not installed'''
       snake_case__						= snake_case__						= '''not found'''
       if is_accelerate_available():
         import accelerate
         from accelerate.commands.config import default_config_file, load_config_from_file

         snake_case__						= accelerate.__version__
         # Get the default from the config file.
         if self._accelerate_config_file is not None or os.path.isfile(_a   ):
           snake_case__						= load_config_from_file(self._accelerate_config_file   ).to_dict()

         snake_case__						= (
             '''\n'''.join([F"""\t- {prop}: {val}""" for prop, val in accelerate_config.items()]   )
             if isinstance(_a     ,		_a   )
             else F"""\t{accelerate_config}"""
         )

       snake_case__						= '''not installed'''
       snake_case__						= '''NA'''
       if is_torch_available():
         import torch

         snake_case__						= torch.__version__
         snake_case__						= torch.cuda.is_available()

       snake_case__						= '''not installed'''
       snake_case__						= '''NA'''
       if is_tf_available():
         import tensorflow as tf

         snake_case__						= tf.__version__
         try:
           # deprecated in v2.1
           snake_case__						= tf.test.is_gpu_available()
         except AttributeError:
           # returns list of devices, convert to bool
           snake_case__						= bool(tf.config.list_physical_devices('''GPU'''   )   )

       snake_case__						= '''not installed'''
       snake_case__						= '''not installed'''
       snake_case__						= '''not installed'''
       snake_case__						= '''NA'''
       if is_flax_available():
         import flax
         import jax
         import jaxlib

         snake_case__						= flax.__version__
         snake_case__						= jax.__version__
         snake_case__						= jaxlib.__version__
         snake_case__						= jax.lib.xla_bridge.get_backend().platform

       snake_case__						= {
           '''`transformers` version''': version,
           '''Platform''': platform.platform(),
           '''Python version''': platform.python_version(),
           '''Huggingface_hub version''': huggingface_hub.__version__,
           '''Safetensors version''': F"""{safetensors_version}""",
           '''Accelerate version''': F"""{accelerate_version}""",
           '''Accelerate config''': F"""{accelerate_config_str}""",
           '''PyTorch version (GPU?)''': F"""{pt_version} ({pt_cuda_available})""",
           '''Tensorflow version (GPU?)''': F"""{tf_version} ({tf_cuda_available})""",
           '''Flax version (CPU?/GPU?/TPU?)''': F"""{flax_version} ({jax_backend})""",
           '''Jax version''': F"""{jax_version}""",
           '''JaxLib version''': F"""{jaxlib_version}""",
           '''Using GPU in script?''': '''<fill in>''',
           '''Using distributed or parallel set-up in script?''': '''<fill in>''',
       }

       print('''\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n'''   )
       print(self.format_dict(_a   )   )

       return info







     @staticmethod
     def 	SCREAMING_SNAKE_CASE__						(					_a:Tuple   ):
       return "\n".join([F"""- {prop}: {val}""" for prop, val in d.items()]   ) + "\n"





import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch


if is_vision_available():
 from transformers import TvltImageProcessor

if is_speech_available():
 from transformers import TvltFeatureExtractor

from transformers import TvltProcessor

@require_torch
class     A__   (       unittest.TestCase      ):







    """simple docstring"""

    def 						a_     (			self       ):
         snake_case											=							'''ZinengTang/tvlt-base'''
         snake_case											=							tempfile.mkdtemp()
    def 						a_     (			self     ,   **__snake_case       ):
         return TvltImageProcessor.from_pretrained(self.checkpoint     ,   **__snake_case       )
    def 						a_     (			self     ,   **__snake_case       ):
         return TvltFeatureExtractor.from_pretrained(self.checkpoint     ,   **__snake_case       )
    def 						a_     (			self       ):
         shutil.rmtree(self.tmpdirname       )
    def 						a_     (			self       ):
         snake_case											=							self.get_image_processor()
         snake_case											=							self.get_feature_extractor()

         snake_case											=							TvltProcessor(image_processor=__snake_case     ,   feature_extractor=__snake_case       )
         processor.save_pretrained(self.tmpdirname       )
         snake_case											=							TvltProcessor.from_pretrained(self.tmpdirname       )

         self.assertIsInstance(processor.feature_extractor     ,   __snake_case       )
         self.assertIsInstance(processor.image_processor     ,   __snake_case       )
    def 						a_     (			self       ):
         snake_case											=							self.get_image_processor()
         snake_case											=							self.get_feature_extractor()

         snake_case											=							TvltProcessor(image_processor=__snake_case     ,   feature_extractor=__snake_case       )

         snake_case											=							np.ones([1_2_0_0_0]       )

         snake_case											=							feature_extractor(__snake_case     ,   return_tensors='''np'''       )
         snake_case											=							processor(audio=__snake_case     ,   return_tensors='''np'''       )

         for key in audio_dict.keys():
              self.assertAlmostEqual(audio_dict[key].sum()     ,   input_processor[key].sum()     ,   delta=1E-2       )
    def 						a_     (			self       ):
         snake_case											=							self.get_image_processor()
         snake_case											=							self.get_feature_extractor()

         snake_case											=							TvltProcessor(image_processor=__snake_case     ,   feature_extractor=__snake_case       )

         snake_case											=							np.ones([3, 2_2_4, 2_2_4]       )

         snake_case											=							image_processor(__snake_case     ,   return_tensors='''np'''       )
         snake_case											=							processor(images=__snake_case     ,   return_tensors='''np'''       )

         for key in image_dict.keys():
              self.assertAlmostEqual(image_dict[key].sum()     ,   input_processor[key].sum()     ,   delta=1E-2       )
    def 						a_     (			self       ):
         snake_case											=							self.get_image_processor()
         snake_case											=							self.get_feature_extractor()

         snake_case											=							TvltProcessor(image_processor=__snake_case     ,   feature_extractor=__snake_case       )

         snake_case											=							np.ones([1_2_0_0_0]       )
         snake_case											=							np.ones([3, 2_2_4, 2_2_4]       )

         snake_case											=							processor(audio=__snake_case     ,   images=__snake_case       )

         self.assertListEqual(list(inputs.keys()       )     ,   ['''audio_values''', '''audio_mask''', '''pixel_values''', '''pixel_mask''']       )

         # test if it raises when no input is passed
         with pytest.raises(__snake_case       ):
              processor()




    def 						a_     (			self       ):
         snake_case											=							self.get_image_processor()
         snake_case											=							self.get_feature_extractor()

         snake_case											=							TvltProcessor(image_processor=__snake_case     ,   feature_extractor=__snake_case       )

         self.assertListEqual(
             processor.model_input_names     ,   image_processor.model_input_names + feature_extractor.model_input_names     ,   msg='''`processor` and `image_processor`+`feature_extractor` model input names do not match'''     ,   )




